Lymph node biopsy is a primary means of staging breast cancer, yet standard pathological techniques are time-consuming and typically sample less than 1% of the total node volume. A low-cost fluorescence optical projection tomography (OPT) protocol is demonstrated for rapid imaging of whole lymph nodes in three dimensions. The relatively low scattering properties of lymph node tissue can be leveraged to significantly improve spatial resolution of lymph node OPT by employing angular restriction of photon detection. It is demonstrated through porcine lymph node metastases models that simple filtered-backprojection reconstruction is sufficient to detect and localize 200-μm-diameter metastases (the smallest clinically significant) in 1-cm-diameter lymph nodes.
Sentinel lymph node biopsy is a primary mean of staging cancer; however, the time-intensive nature of standard pathology limits the volume of the node that can be assessed. As a result, micrometastases can be missed, which have been shown to affect treatment decisions and therefore clinical outcomes. Optical imaging offers a potential solution for improved sensitivity and larger tissue evaluation, but an understanding of optical properties is necessary because of the high scattering nature of biological tissue. Here, time-domain optical imaging and measures of transmittance are used to characterize the optical properties of porcine lymph nodes at 685 nm and 780 nm. Results demonstrated values comparable to that of other soft biological tissue (685 nm: μa = 0.09 ± 0.01cm-1 , μs’ = 2.60 ± 0.42 cm-1 , g = 0.95; 780 nm: μa = 0.24 ± 0.10cm-1 , μs’ = 3.35 ± 0.14 cm-1 , g = 0.92). Based on these coefficients, optical properties of TiO2 were investigated so that a protocol to fabricate a lymph node tissue-mimicking phantom could be defined.
With tissue samples less than 1 mm in thickness, optical projection tomography (OPT) has proven to be a very powerful imaging modality that can achieve high spatial resolution images. This high resolution is achieved by collecting the photons with non-significant scattering through 1 mm of tissue, the so-called diffusion limit. But, with samples thicker than 1 mm, scattered photons dominate and the highly resolved images give way to significantly “blurred” images in OPT[1]. However, as increased scattering relates to increased time of travel of the photons so time-domain OPT has been used to only collect the early-arriving photons that have travelled a more direct route through the tissue to reduce detection of scattered photons. Yet very early photons are extremely rare compared to scattered photons. Our recent suggested early photon count rates can be significantly enhanced by running the detector in a “deadtime” regime where the deadtime incurred by early-arriving photons acts as a shutter to later-arriving scattered photons[2]. In this work, we will demonstrate that running in the deadtime also had the unexpected advantage of significantly reducing the number of background photons detected. Proposed approach increases the early photon detection rate by 3-orders-of-magnitude in comparison with conventional approaches in 4-mm thick tissues with 780 nm light while the laser power is far below the level that would significantly damage the tissue. In addition, the signal to background (caused by after pulsing) was improved by 70-fold compared to conventional approaches designed to collect an equal number of early photons.
Clinical symptoms of diabetic retinopathy are not detectable until damage to the retina reaches an irreversible stage, at least by today’s treatment standards. As a result, there is a push to develop new, “sub-clinical” methods of predicting the onset of diabetic retinopathy before the onset of irreversible damage. With diabetic retinopathy being associated with the accumulation of long-term mild damage to the retinal vasculature, retinal blood vessel permeability has been proposed as a key parameter for detecting preclinical stages of retinopathy. In this study, a kinetic modeling approach used to quantify vascular permeability in dynamic contrast-enhanced medical imaging was evaluated in noise simulations and then applied to retinal videoangiography data in a diabetic rat for the first time to determine the potential for this approach to be employed clinically as an early indicator of diabetic retinopathy. Experimental levels of noise were found to introduce errors of less than 15% in estimates of blood flow and extraction fraction (a marker of vascular permeability), and fitting of rat retinal fluorescein angiography data provided stable maps of both parameters.
One of the major challenges in the complete resection of cancer is the difficulty of distinctly classifying tumor and healthy tissue. This paper investigates the capability of competing kinetic modeling approaches for identifying different tissue types based on differential cell-surface receptor expressions. These approaches require fresh resected tissues to be stained with a mixture of two probes: one targeted to a cancer specific cell-surface receptor, and another left “untargeted” to account for nonspecific retention of the targeted agent, with subsequent repeated rinsing and imaging of the probe concentrations. Analysis of the results were carried out in simulations and in animal experiments for the cancer target, epidermal growth factor receptor (EGFR), a cell surface receptor overexpressed by many cancers. In the animal experiments, subcutaneous xenografts of human glioma (U251; moderate EGFR) and human epidermoid (A431; high EGFR) tumors, grown in six athymic mice, were excised and stained with an EGFR targeted surface-enhanced Raman scattering nanoparticle (SERS NP) and untargeted SERS NP pair. The salient finding in this study was that significant non-specific retention was observed for the EGFR targeted probe [anti-EGFR antibody labeled with a surface-enhanced Raman scattering (SERS) nanoparticle], but could be corrected for by the equivalent non-specific retention of the untargeted probe (isotype control antibody labeled with a different SERS nanoparticle). Once this non-specific binding was accounted for, the kinetic model was able to predict the expected differences in EGFR concentration among different tissue types: healthy, U251, and A431 in accordance with an ex vivo flow cytometry analysis, successfully classifying different tissue types.
Immunofluorescence staining is a robust way to visualize the distribution of targeted biomolecules invasively in in fixed tissues and tissue culture. Despite the fact that these methods has been a well-established method in fixed tissue imaging for over 70 years, quantification of receptor concentration still simply assumes that the signal from the targeted fluorescent marker after incubation and sufficient rinsing is directly proportional to the concentration of targeted biomolecules, thus neglecting the experimental inconsistencies in incubation and rinsing procedures and assuming no, nonspecific binding of the fluorescent markers. This work presents the first imaging approach capable of quantifying the concentration of cell surface receptor on cancer cells grown in vitro based on compartment modeling in a nondestructive way. The approach utilizes a dual-tracer protocol where any non-specific retention or variability in incubation and rinsing of a receptor-targeted imaging agent is corrected by simultaneously imaging the retention of a chemically similar, “untargeted” imaging agent. Various different compartment models were used to analyze the data in order to find the optimal procedure for extracting estimates of epidermal growth factor receptor (EGFR) concentration (a receptor overexpressed in many cancers and a key target for emerging molecular therapies) in tissue cultures with varying concentrations of human glioma cells (U251). Preliminary results demonstrated a need to model nonspecific binding of both the targeted and untargeted imaging agents used. The approach could be used to carry out the first repeated measures of cell surface receptor dynamics during 3D tumor mass development, in addition to the receptor response to therapies.
Photodynamic therapy (PDT) has shown promising results in targeted treatment of cancerous cells by developing localized toxicity with the help of light induced generation of reactive molecular species. The efficiency of this therapy depends on the product of the intensity of light dose and the concentration of photosensitizer (PS) in the region of interest (ROI). On account of this, the dynamic and variable nature of PS delivery and retention depends on many physiological factors that are known to be heterogeneous within and amongst tumors (e.g., blood flow, blood volume, vascular permeability, and lymph drainage rate). This presents a major challenge with respect to how the optimal time and interval of light delivery is chosen, which ideally would be when the concentration of PS molecule is at its maximum in the ROI. In this paper, a predictive algorithm is developed that takes into consideration the variability and dynamic nature of PS distribution in the body on a region-by-region basis and provides an estimate of the optimum time when the PS concentration will be maximum in the ROI. The advantage of the algorithm lies in the fact that it predicts the time in advance as it takes only a sample of initial data points (~12 min) as input. The optimum time calculated using the algorithm estimated a maximum dose that was only 0.58 ± 1.92% under the true maximum dose compared to a mean dose error of 39.85 ± 6.45% if a 1 h optimal light deliver time was assumed for patients with different efflux rate constants of the PS, assuming they have the same plasma function. Therefore, if the uptake values of PS for the blood and the ROI is known for only first 12 minutes, the entire curve along with the optimum time of light radiation can be predicted with the help of this algorithm.
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